Trace Metals in Soils from La Paz Valley (B.C.S., Mexico), Are they Contaminated?
Bibliographic record
Abstract
Irrigation for agricultural purposes accounts for between 60 to 90% of the total water used for human activities, making it the largest consumer of freshwater on the planet. One of the most common practices for disposing of domestic wastewater has been its direct discharge, without treatment, into surface water bodies and the soil, resulting in public health issues and environmental problems, such as soil contamination. The aim of this study was to evaluate the contamination index of four soil samples in the La Paz Valley by analyzing the total concentration of trace element (cadmium, chromium, cooper, nickel, lead and zinc) to determinate contamination levels using the geoaccumulation index (Igeo). Additionally, four chemical variables were determined, organic matter (OM), total nitrogen, nitrates, and pH, to establish whether a relationship exists between the trace elements and the chemical variables. All trace element concentration ranges were below the total reference concentration according to the Canadian Of ficial Norm, and the Igeo values for the trace metals indicated uncontaminated concentrations. Furthermore, the average OM percentages were categorized as low and to medium across the four sites (0.72 to 1.70%), two sites recorded very low mean nitrogen concentration (0.05-0.08%), high nitrate concentrations were observed at site A (62.7 to 146 mg kg−1) and B (18.1 to 142.9 mg kg−1), and all the soils were alkaline (pH = 7.21 – 8.37). Significant positive correlations were found between OM vs Zn, Cu, and Ni, likely due to soil process that af fect the solubility and availability of these elements, or because these metals share common sources. Lastly, Cu and soil pH showed a significant positive correlation, influenced by several interaction factors. The varying irrigation characteristics at these four sites could af fect the chemical properties, but they do not appear to generate contamination, at least not in the elements analyzed in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".